Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель SARIMA× | Авторегрессионная модель (AR)× | Модель скользящего среднего (MA)× | |
|---|---|---|---|
| Область | Эконометрика | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model | Regression model |
| Год появления≠ | 1970 (first edition); 1976 (revised) | 1970s (popularised 1976) | 1970 |
| Автор метода≠ | Box, Jenkins, and Reinsel | George E. P. Box and Gwilym M. Jenkins | Box and Jenkins |
| Тип≠ | Seasonal time series model | Time series model | Linear time series model |
| Основополагающий источник≠ | Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0130607744 | Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0816211043 | Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0130607744 |
| Другие названия | SARIMA, seasonal ARIMA, Box-Jenkins seasonal model, ARIMA with seasonal component | AR model, AR(p) model, autoregression, AR process | MA model, MA(q) process, moving-average process, Box-Jenkins MA |
| Связанные≠ | 5 | 6 | 5 |
| Сводка≠ | SARIMA extends ARIMA by adding seasonal autoregressive and moving-average operators to capture repeating patterns at fixed intervals — such as monthly, quarterly, or annual cycles. Denoted SARIMA(p,d,q)(P,D,Q)s, it is the standard workhorse for univariate seasonal time series forecasting in econometrics, economics, and official statistics. | An autoregressive model of order p — AR(p) — expresses the current value of a time series as a linear function of its own p most recent past values plus a white-noise error. It is the building block of the Box-Jenkins family of time-series models and is widely used for forecasting stationary economic and financial series. | The Moving Average model of order q — written MA(q) — expresses the current value of a time series as a linear combination of the current and past random shocks (innovations). Unlike the AR model which uses lagged values of the series itself, the MA model uses lagged error terms, making it well-suited for capturing short-lived disturbances that dissipate over q periods. |
| ScholarGateНабор данных ↗ |
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